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    This study introduces RuleMatrix, an interactive visualization tool that helps domain experts understand and validate machine learning models without needing deep technical knowledge. RuleMatrix makes complex AI systems more accessible for broader application and verification.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Machine learning (ML) adoption is increasing, driving demand for interpretable and transparent AI systems.
    • Existing ML visualization tools often cater to developers, neglecting domain experts with limited ML knowledge.
    • Domain experts are increasingly expected to collaborate with and validate ML systems in their fields.

    Purpose of the Study:

    • To present an interactive visualization technique for users with limited machine learning expertise.
    • To enable domain experts to understand, explore, and validate predictive models.
    • To bridge the gap between ML developers and domain experts in using AI tools.

    Main Methods:

    • Treating the ML model as a black box.
    • Extracting a standardized, rule-based knowledge representation from the model's input-output behavior.
    • Designing RuleMatrix, a novel matrix-based visualization for exploring and verifying extracted rules and the overall model.

    Main Results:

    • RuleMatrix provides an accessible interface for non-experts to interact with ML models.
    • The visualization facilitates the navigation and verification of model logic through standardized rules.
    • Effectiveness was demonstrated through two use cases and a usability study.

    Conclusions:

    • RuleMatrix enhances the interpretability of machine learning models for domain experts.
    • The technique empowers users with little ML knowledge to effectively validate AI systems.
    • This approach promotes greater trust and collaboration between domain experts and AI.